Abstract
The inactivation of four different microorganisms, Escherichia coli, Bacillus mycoides, Staphylococcus aureus and Candida albicans, inoculated in simulated (SWW) and real winery wastewaters (RWW), was assessed by the first time using free sulphate and hydroxyl radicals from photolytic (UV-A LED radiation; 370 nm) and metal [Fe(II) or Co(II)] activation of peroxymonosulphate (PMS). The experimental conditions tested were [PMS] = 0.1 mM and [Fe(II) or Co(II)] = 0.1 mM and pH 5.0 for the inactivation of microorganisms in SWW. However, due to the complexity of the water matrix, not unexpectedly, a fivefold concentration of reagents was required to inactivate the same organisms in RWW. In addition, compared to the bacteria, the fungus C. albicans presented a higher oxidative stress resistance to the treatments, and different experimental conditions were necessary to inactivate these cells. After 90 min, the photolytic activation of PMS through UV-A LED radiation achieved complete inactivation of E. coli, followed by S. aureus (approximate to 4 log) and B. mycoides (approximate to 3 log). Total inactivation of C. albicans was also achieved, but with higher dosages of PMS (10 mM). The metal activation of PMS through the use of a transition metal [Fe(II) or Co(II)] accelerated the inactivation rate, particularly in the first minutes of exposure time. These treatments reached between 1 and 3 log inactivation of microorganisms in the first minute of the experiment. In addition, the use of Co(II) as promoter in the activation of PMS, was more effective in the inactivation of S. aureus and C. albicans than activation with Fe(II). Since linear mathematical models do not adjust satisfactorily to inactivation results in all cases, different mathematical models were tested to fit the experimental inactivation data. In general, the Hom model correctly fits the inactivation results of the four microorganisms in all applied treatments. However, other models such as Biphasic and Double Weibull fit acceptably as well.

Abstract

Abstract
Optical properties of biological tissues are unique and may be used for tissue identification, tissue discrimination or even to identify pathologies. Early stage colorectal cancer evolves from adenomatous polyps that arise in the inner layer of the colorectal tube - the mucosa. The identification of different optical properties between healthy and pathological colorectal tissues might be used to identify different tissue components and to develop an early stage diagnosis method using optical technologies. Since most of the biomedical optics techniques use light within the visible and near infrared wavelength ranges, we used the inverse adding-doubling method to make a fast estimation of the optical properties of colorectal mucosa and early stage adenocarcinoma between 400 and 1000 nm. The estimated wavelength dependencies have provided information about higher lipid content in healthy mucosa and higher blood content in pathological tissue. Such data has also indicated that the wavelength dependence of the scattering coefficient for healthy mucosa is dominated by Rayleigh scattering and for pathological mucosa it is dominated by Mie scattering. Such difference indicates smaller scatterer size in healthy mucosa tissue. Such information can now be used to develop new diagnosis or treatment methods for early cancer detection or removal. One possibility is to use optical clearing technique to improve tissue transparency and create localized and temporary tissue dehydration for image contrast improvement during diagnosis or polyp laser removal. Such techniques can now be developed based on the different results that we have found for healthy and pathological colorectal mucosa.

Abstract
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This algorithm is part of several other FIS algorithms implemented in the Fuzzy Rule-Based Systems (FRBS) package of R. The ANN algorithms and Support Vector Machine (SVM), both widely used for solving regression problems, are also used in this study to allow the comparison of results. Results show that HyFIS presents higher performance when compared to the ANN and SVM, when applied to real data of Florianopolis, Brazil, which helps to reinforce the potential of this algorithm in solving the solar intensity forecasting problems. © 2016 IEEE.

Abstract
In this study an application to assist people with speech impairments in their speech therapy sessions is presented. AppVox simulates a vocalizer (audio stimulus feature) that can be used to train speech by repeating different words. In this paper, we aim at presenting the application as an assistive technology option and assess if this is a usable option for digital interaction for children with speech impairment. To assess the application we present a case study in which the participants were asked to perform tasks using the AppVox application. The results showed that this group of participants attained a good performance when interacting with the application.

Abstract
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1-4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8-11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.